This week I did not have a lot of time to update my portfolio due to other courses, I have processed some data, which updated my plots and provided some new interesting things:
There was a problem in de database in which a lot of the albums did not make my time period subsets (a lot of album release years were NAs for some reason, so I transformed the exact release data from days to years and used that instead), this yielded more data
I have decided to omit the hiatus years of Miles (1975 - 1980), since Spotify only has 1 album of that period, which is a re-release of an earlier period
I have filtered out a lot of compilation albums, remastered releases and later releases based on research to try to conclude original recordings exclusively
I have standardized everything song and made a list of typical songs, I have not yet had the time to analyse these songs
I have added and modified some interpretations to plots
Firstly I have made composite standardized z-scores based on acousticness, danceability, valence, energy and tempo. This allows me to filter out the most typical and atypical songs of each time period. The following is a list of typical songs (z < 0.01)
Most typical songs per time (based on tempo, valence, danceability, acousticness and energy) 40ies: - Half Nelson
50ies: - Boplicity, Birth of the Cool
First Quintet - ‘Round Midnight
Second Quintet: - Dolores
Fusion: - Willie Nelson - Live at Fillmore East, New York, NY - June 20, 1970
Final years: - Freaky Deaky
[1] 8 29
[1] 33 43
[1] 145 167
[1] 32 52
[1] 187
[1] 34 64
Looking at the self-similarity matrix, the structure can very clearly be derived by the pattern. I’ve added timestamps in the table to show the individual sections of the song and they align perfectly. The setup is a quintet, with trumpet, piano and saxophone taking solo’s and the rhythm section (piano, bass and drums) accompanying throughout the piece. The reason that this self-similarity matrix works so well is because only one instrument is solo-ing and the accompaniment stays the same. This makes the track very well structured and therefore easy to analyse with SS-matrices.
Have a listen to the song: